Investigation of the risk and preventive factors for progression of mild cognitive impairment to dementia: a 6-year follow-up study
Bibliographic record
Abstract
Abstract Background: To investigate the cognition change of mild cognitive impairment (MCI) during a 6-year follow-up, and to evaluate the preventive and risk factors for MCI progression to dementia. Methods: This cross-sectional study was based on the results of the epidemiological survey in 2011 (No. PKJ2010-Y26). A total of 441 MCI individuals, 60 years and above were involved. Cognitive function was measured by the mini-mental status examination (MMSE), clinical dementia rating (CDR), montreal cognitive assessment (MoCA), and daily living scale (ADL). The association between demographic characteristics and MCI outcomes were evaluated using single-and multi-factor ordered logistic regression analysis models. Results: Exclusion of the relocated community, the final follow-up rate was 43.8%. Individuals who were older, had more children, not in marriage, and with high income were easily lost to follow-up. Of the 441 MCI, 77 progressed to dementia (MCIp, 17.5%, 95% CI: 14.4-21.6%), 356 remained stable (MCIs, 80.7%, 95% CI: 77.0-88.4%), and 8 reverted to normal cognition (MCIr, 1.8%, 95% CI: 0.6-3.0%) at follow-up in 2017. Diabetes (P=0.047) and past occupations as managers (P=0.028) increased the risk of MCI progression to dementia. While, high education (P=0.006) was the protective factor of MCI progression. Conclusions: High education, nondiabetic, and past occupation as a technical staff might prevent the progression of MCI to dementia. Keywords: Mild cognitive impairment; dementia; ordered logistic regression analysis; education; diabetes; past occupation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".